The objective of this application is to support the career development of Linnea Polgreen, PhD. Her long-term goal is to become an independent investigator with a research program focused on developing methods for determining the best treatment options based on observational data. She has assembled a team of experienced mentors, including both clinical and quantitative researchers. These mentors not only bring diverse and complementary areas of expertise, but also, all of them have been successful mentors of students and young faculty. With this team of mentors, she will gain insight into what clinical questions she should attempt to answer using quantitative methods, and how to answer them. Although randomized controlled trials (RCTs) are the gold standard for determining treatment effectiveness, RCTs are difficult to perform: they are expensive and slow. In the absence of findings from RCTs, observational data are often used to inform treatment decisions. There are a number of strategies available to analyze treatment outcomes using observational data, but exactly which strategy is likely to be most accurate is currently unclear. Her objective in this application is to develop a general framework using methods from economics, epidemiology and computer science and outcome-based observational data to determine treatment effectiveness for medical interventions in general. Specifically, she will use Medicare Part A, B and D data from a cohort of acute myocardial infarction (AMI) patients treated with angiotensin-converting enzyme (ACE) inhibitors or angiotensin II receptor blockers (ARB) to prevent subsequent AMIs, followed 1 year prior to, and up to 4 years post, the index AMI. For Aim 1, she will identify the approach that most accurately reflects ACE inhibitor/ARB outcomes as given by previous RCTs. For Aim 2, she will identify the approach that most accurately estimates treatment effectiveness for patients not generally eligible for RCTs. Upon successful completion of these aims, she expects to have developed a framework for identifying the advantages and disadvantages of different methods for estimating treatment effectiveness using observational data for a broad range of cardiac and pulmonary diseases.